AquaBEHER (version 1.4.0)

seasFcstQBR: Seasonal Forecast of Wet Season Calendar (WSC) from Tercile Rainfall Probabilities using Quantile Bin Resampling (QBR)

Description

Generates seasonal forecasts for Wet Season Calendar (WSC) variables (onset or cessation) using tercile seasonal rainfall probabilities

Usage

seasFcstQBR(
  hisYearStart,
  hisYearEnd,
  rainTerc,
  seasRain,
  hisWSCvar,
  fcstVarName,
  tercileMethod
)

Value

A data frame containing the tercile probabilities for the WSC variable ("BelowNormal", "Normal", and "AboveNormal").

Arguments

hisYearStart

Starting year (YYYY) for historical resampling.

hisYearEnd

Ending year (YYYY) for historical resampling.

rainTerc

A data frame with tercile probabilities for rainfall. Columns should be named "T1" (below normal), "T2" (normal), and "T3" (above normal).

seasRain

A data frame containing seasonal rainfall data with columns

hisWSCvar

A data frame containing historical WSC simulations. This can be the output from the calcSeasCal function or a similar data frame with required columns.

fcstVarName

A character string indicating the WSC variable to forecast ("Onset" or "Cessation").

tercileMethod

options are "quantiles" or "fixedValues"

Details

Uses QBR (Quantile Bin Resampling) to produce forecasts for onset or cessation of the rainy season. It first categorizes historical WSC simulations based on seasonal rainfall terciles and then resamples based on given rainfall probabilities to generate ensemble forecasts.

References

• MacLeod D, Quichimbo EA, Michaelides K, Asfaw DT, Rosolem R, Cuthbert MO, et al. (2023) Translating seasonal climate forecasts into water balance forecasts for decision making. PLOS Clim 2(3): e0000138. https://doi.org/10.1371/journal.pclm.0000138

• van den Dool HM. A New Look at Weather Forecasting through Analogues. Monthly Weather Review. 1989; 117(10):2230–2247. https://doi.org/10.1175/1520-0493(1989)117%3C2230:ANLAWF%3E2.0.CO;2

See Also

calcSeasCal, calcWatBal

Examples

# \donttest{
library(dplyr)

## Load example data:
data(AgroClimateData)

## Estimate daily PET:
PET <- calcEto(AgroClimateData, method = "PM", Zh = 10)

## Add the estimated PET 'ET.Daily' to a new column in AgroClimateData:
AgroClimateData$Eto <- PET$ET.Daily

## Estimate daily water balance for the soil having 100mm of WHC:
watBal.list <- calcWatBal(data = AgroClimateData, soilWHC = 100)
watBal <- watBal.list$data

## seasonal calendar is estimated for the onset window ranges from
## 01 September to 31 January having a soil with 100mm of WHC:

soilWHC <- 100
onsetWind.start <- "09-01"
onsetWind.end <- "01-31"
cessaWind.end <- "06-30"

seasCal.dF <- calcSeasCal(
  data = watBal, onsetWind.start, onsetWind.end,
  cessaWind.end, soilWHC
)

## Tercile Rainfall Probabilities of seasonal Forecast for OND, 2023:
rainTerc <- data.frame(T1 = 0.15, T2 = 0.10, T3 = 0.75)

## Summarize rainfall data for October to December:
seasRain <- AgroClimateData %>%
  filter(Month %in% c(10, 11, 12)) %>%
  group_by(Year) %>%
  summarize(sRain = sum(Rain))

## Start of the historical resampling year
hisYearStart <- 1991

## End of the historical resampling year
hisYearEnd <- 2022

## Historical WSC Simulations:
hisWSCvar <- seasCal.dF

## WSC variable to forecast:
fcstVarName <- "Onset"
tercileMethod <- "quantiles"

SeasFcst.dF <- seasFcstQBR(
  hisYearStart, hisYearEnd, rainTerc,
  seasRain, hisWSCvar, fcstVarName,
  tercileMethod
)
# }